63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
# Copyright 2024 MIT Han Lab
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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from typing import Union
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import torch
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import torch.nn as nn
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from torch.nn.modules.batchnorm import _BatchNorm
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__all__ = ["init_modules"]
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def init_modules(model: Union[nn.Module, list[nn.Module]], init_type="trunc_normal") -> None:
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_DEFAULT_INIT_PARAM = {"trunc_normal": 0.02}
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if isinstance(model, list):
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for sub_module in model:
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init_modules(sub_module, init_type)
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else:
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init_params = init_type.split("@")
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init_params = float(init_params[1]) if len(init_params) > 1 else None
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if init_type.startswith("trunc_normal"):
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init_func = lambda param: nn.init.trunc_normal_(
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param, std=(_DEFAULT_INIT_PARAM["trunc_normal"] if init_params is None else init_params)
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)
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elif init_type.startswith("normal"):
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init_func = lambda param: nn.init.normal_(
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param, std=(_DEFAULT_INIT_PARAM["trunc_normal"] if init_params is None else init_params)
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)
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else:
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raise NotImplementedError
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for m in model.modules():
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if isinstance(m, (nn.Conv2d, nn.Linear, nn.ConvTranspose2d)):
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init_func(m.weight)
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.Embedding):
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init_func(m.weight)
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elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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else:
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weight = getattr(m, "weight", None)
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bias = getattr(m, "bias", None)
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if isinstance(weight, torch.nn.Parameter):
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init_func(weight)
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if isinstance(bias, torch.nn.Parameter):
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bias.data.zero_() |